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- # +
- _base_ = '../_base_/datasets/occlude_face.py'
- norm_cfg = dict(type='SyncBN', requires_grad=True)
- model = dict(
- type='EncoderDecoder',
- pretrained='open-mmlab://resnet101_v1c',
- backbone=dict(
- type='ResNetV1c',
- depth=101,
- num_stages=4,
- out_indices=(0, 1, 2, 3),
- dilations=(1, 1, 2, 4),
- strides=(1, 2, 1, 1),
- norm_cfg=dict(type='SyncBN', requires_grad=True),
- norm_eval=False,
- style='pytorch',
- contract_dilation=True),
- decode_head=dict(
- type='DepthwiseSeparableASPPHead',
- in_channels=2048,
- in_index=3,
- channels=512,
- dilations=(1, 12, 24, 36),
- c1_in_channels=256,
- c1_channels=48,
- dropout_ratio=0.1,
- num_classes=2,
- norm_cfg=dict(type='SyncBN', requires_grad=True),
- align_corners=False,
- loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
- sampler=dict(type='OHEMPixelSampler', thresh=0.7, min_kept=10000)),
- auxiliary_head=dict(
- type='FCNHead',
- in_channels=1024,
- in_index=2,
- channels=256,
- num_convs=1,
- concat_input=False,
- dropout_ratio=0.1,
- num_classes=2,
- norm_cfg=dict(type='SyncBN', requires_grad=True),
- align_corners=False,
- loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
- train_cfg=dict(),
- test_cfg=dict(mode='whole'))
- log_config = dict(
- interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)])
- dist_params = dict(backend='nccl')
- log_level = 'INFO'
- load_from = None
- resume_from = None
- workflow = [('train', 1)]
- cudnn_benchmark = True
- optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
- optimizer_config = dict()
- lr_config = dict(policy='poly', power=0.9, min_lr=0.0001, by_epoch=False)
- runner = dict(type='IterBasedRunner', max_iters=30000)
- checkpoint_config = dict(by_epoch=False, interval=400)
- evaluation = dict(
- interval=400, metric=['mIoU', 'mDice', 'mFscore'], pre_eval=True)
- auto_resume = False
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